Temporomandibular disorder confounders in motor vehicle accident patients
Bibliographic record
Abstract
Background: Motor vehicle accidents (MVA) are associated with the onset of temporomandibular disorder (TMD) symptoms. However, diagnosing TMD-related pain is challenging due to various entities that can refer pain to the region. This study aims to identify prevalent radiographic confounders to pain diagnosis in MVA patients who were subsequently referred for temporomandibular joint imaging using cone-beam computed tomography (CBCT) by comparing these patients to a cohort of patients without MVA history. Methods: CBCTs of 738 temporomandibular joints were reviewed, with cases stratified by MVA history. This research explored the demographics and calculated the prevalence of radiographic confounders (RC) in each category, comparing the findings for both groups. The chi-square test was used to assess statistical significance. Results: Patients in the MVA cohort (n = 151, mean age = 41.3 years, S.D (Standard Deviation) = 13.3 years) averaged 1.10 confounders/patient compared to a significantly lower 0.68 confounders/patient in the non-MVA cohort (n = 218, mean age = 33.6 years, S.D = 18.2 years). The most frequently identified RCs include sinus pathologies (39.1% (MVA) vs. 28.0% (non-MVA), p = 0.025) and endodontic lesions (22.5% (MVA) vs.10.1% (non-MVA), p = 0.001). Conclusions: Clinicians must be vigilant about confounders when managing patients suspected of TMD. We recommend patients undergo a complete dental evaluation before being referred to a specialist to avoid unnecessary medical costs and treatment delays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".